The increasing emissions of greenhouse gases (GHG) and pollutants like particulate matter and nitrogen oxides (NOx) have led to environmental concerns. Hybrid and electric powertrains are being introduced as means to reduce pollutant emissions, especially at the local level. Additionally, the finite availability of fossil fuel sources, which are used to produce gasoline and diesel, highlights the need for alternative technical solutions. One approach to partly address these issues is lightweighting, which involves reducing the weight of vehicles to minimize their impact during the use phase. Mathematical models are employed to simulate the longitudinal dynamics of vehicles and estimate the energy required to accomplish driving missions. Appropriate metrics have been developed to quantify energy-saving effects that, in addition, can support the decision making, design, and development phase of future vehicles. To facilitate this process, it would be useful to build a database of ERV (Energy Reduction Value) and FRV (Fuel Reduction Value) figures derived through a unified procedure. Such a database would be useful in evaluating the effectiveness of vehicle lightweighting and its impact on energy consumption and pollutant emissions. The last phase of the analysis is the assessment of the overall reduction in the environmental impact of the vehicle throughout its life cycle by using the LCA (Life Cycle Assessment) approach. From this study, it was possible to conclude that lightweighting can be an appropriate solution to improve the energy efficiency of vehicles and that appropriate metrics, can support the development of new car models. The potential to integrate enhanced energy efficiency, lower emissions, and higher safety features into our everyday vehicles would represent a significant advancement in the automotive industry. There is a gap in the scientific literature on the effects of lightweighting on vehicle dynamics and energy usage which deserves to be investigated.

This paper presents a regenerative braking logic that aims to maximize the recovery of energy during braking without compromising the stability of the vehicle. This model of regenerative braking ensures that the regenerative torque of the electric motor (for front- and rear-wheel drive vehicles) or electric motors (for all-wheel drive vehicles equipped with one motor for each axle) is exploited to the maximum, avoiding the locking of the driving wheels and, subsequently, if necessary, integrating the braking with the traditional braking system. The priority of the logic is that of maximizing energy recovery under braking, followed by the pursuit of optimal braking distribution. This last aspect in particular occurs when there is an integration of braking and, for vehicles with all-wheel drive, also when choosing the distribution of regenerative torque between the two electric motors. The logic was tested via simulation on a front-, rear-, and all-wheel drive compact car, and from the simulations, it emerged that, on the WLTC driving cycle, the logic saved between 29.5 and 30.3% in consumption compared to the same vehicle without regenerative recovery, and 22.6–23.5% compared to a logic commonly adopted on the market. On cycle US06, it saves 23.9–24.4% and 19.0–19.5%, respectively.

Nowadays, the topic of reducing vehicles’ energy consumption is very important. In particular, for electric vehicles, the reduction of energy consumption is necessary to remedy the most critical problems associated with this type of vehicle: the problem of the limited range of the electric traction, also associated with the long recharging times of the battery packs. To reduce use-phase impacts and energy consumptions of vehicles, it is useful to reduce the vehicle mass (lightweighting). The aim of this work is to analyze the parameters of a vehicle which influence the results of lightweighting, in order to provide guidelines for the creation of a vehicle model suitable for studying the effects of lightweighting. This study was carried out through two borderline case models, a compact car and an N1 vehicle, and simulating these through a consolidated vehicle simulation tool useful for consumption estimations. This study shows that the parameters that most influence the outcome of lightweighting are the rolling resistance, the battery pack characteristics, the aerodynamic coefficients, and the transmission efficiency, while the inertia contributions can be considered negligible. An analysis was also carried out with the variation of the driving cycle considered.

Today, reducing vehicle energy consumption is a crucial topic. For electric vehicles, reducing energy consumption is essential to address some of the most critical issues associated with this type of vehicle, such as the limited range of electric powertrains and the long battery recharging times. To lower the environmental impact during the vehicle’s use phase and reduce energy consumption, vehicle mass reduction (lightweighting) is an effective strategy. The objective of this work is to analyze the vehicle parameters that influence lightweighting outcomes on a real driving cycle, representative of the home-to-work travel in northern Italy. In particular, a previous work carried out on standard driving cycles is repeated in order to observe whether it is possible to draw the same conclusions regarding the variability in the lightweighting outcome. This study was conducted using two opposite vehicle models, a compact car and an N1 vehicle, simulated through a well-established vehicle simulation tool for energy consumption estimation. To conduct this analysis, several simulations with variable vehicle mass, and with different vehicle parameters, such as aerodynamics and rolling resistance, were performed to estimate energy consumption across a real-world driving cycle, acquired via GPS on board the vehicle during a home-to-work journey in northern Italy. This study reveals that even for the real driving cycle, as for the WLTC and US06 standards, the parameters that most influence the outcome of the lightening are the rolling resistance, the characteristics of the battery pack, the aerodynamic coefficients, and the efficiency of the transmission. Finally, the standard cycle that best fits with the real one considered in this study is the Artemis Urban Cycle.

This work aims to develop a mathematical model for the simulation of a fuel cell (FC) hybrid powertrain. The work starts from modeling a single cell to obtain information on the entire FC stack. The model obtained was integrated into a simulation tool presented in the literature that simulates the longitudinal dynamics of auxiliary power unit hybrid electric vehicles and fully electric vehicles. Therefore, the integrated model allows the simulation of hybrid vehicles equipped with FC and a battery pack that acts as a peak power source. The tool simulates the mechanical and electrical behavior of the vehicle, introducing an investigation of the power flows relating to the FC and batteries. An appropriate power split logic has been implemented, allowing the correct management of the power distribution between the FC and the batteries. The importance of analyzing FC vehicles’ behavior arises from the recent necessity to find alternative propulsion systems, overcoming the range problems associated with fully electric vehicles. The innovation lies in the versatility and modularity of the model, which is open to modifications and features a low computational burden, making it suitable for testing new solutions by performing first design and sizing calculations.

Due to problems related to environmental pollution and fossil fuels consumption that have not infinite availability, the automotive sector is increasingly moving towards electric powertrains. The most limiting aspect of this category of vehicles is certainly the battery pack, regarding the difficulty in obtaining high range with good performance and low weights. The aim of this work is to provide a simulation tool, which allows for the analysis of the performance of different types of electric and hybrid powertrains, concerning both mechanical and electrical aspects. Through this model it is possible to test different vehicle configurations before prototype realization or to investigate the impact that subsystems’ modifications may have on a vehicle under development. This will allow to speed-up the model-based design process typical for fully electric and hybrid vehicles. The model aims to be at the same time complete but simple enough to lower the simulation time and computational burden so that it can be used in real-time applications, such as driving simulators. All this reduces the time and costs of vehicle design. Validation is also provided, based on a real vehicle and comparison with another consolidated simulation tool. Maximum error on mechanical quantities is proved to be within 5% while on electrical quantities it is always lower than 10%.